TiDAL: Learning Training Dynamics for Active Learning

Active learning (AL) aims to select the most useful data samples from an unlabeled data pool and annotate them to expand the labeled dataset under a limited budget. Especially, uncertainty-based methods choose the most uncertain samples, which are known to be effective in improving model performance. However, AL literature often overlooks training dynamics (TD), defined as the ever-changing model behavior during optimization via stochastic gradient descent, even though other areas of literature have empirically shown that TD provides important clues for measuring the sample uncertainty. In this paper, we propose a novel AL method, Training Dynamics for Active Learning (TiDAL), which leverages the TD to quantify uncertainties of unlabeled data. Since tracking the TD of all the large-scale unlabeled data is impractical, TiDAL utilizes an additional prediction module that learns the TD of labeled data. To further justify the design of TiDAL, we provide theoretical and empirical evidence to argue the usefulness of leveraging TD for AL. Experimental results show that our TiDAL achieves better or comparable performance on both balanced and imbalanced benchmark datasets compared to state-of-the-art AL methods, which estimate data uncertainty using only static information after model training.Comment: ICCV 2023 Camera-Read

Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection

Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the label miscorrection, resulting in unavoidable performance degradation. Moreover, every training step requires at least three back-propagations, significantly slowing down the training speed. To mitigate these issues, we propose a robust and efficient method that learns a label transition matrix on the fly. Employing the transition matrix makes the classifier skeptical about all the corrected samples, which alleviates the miscorrection issue. We also introduce a two-head architecture to efficiently estimate the label transition matrix every iteration within a single back-propagation, so that the estimated matrix closely follows the shifting noise distribution induced by label correction. Extensive experiments demonstrate that our approach shows the best performance in training efficiency while having comparable or better accuracy than existing methods.Comment: ECCV202

Nonvolatile memories using deep traps formed in HfO₂ by Nb ion implantation

We report nonvolatile memories (NVMs) based on deep-energy trap levels formed in HfO₂ by metal ion implantation. A comparison of Nb- and Ta-implanted samples shows that suitable charge-trapping centers are formed in Nb-implanted samples, but not in Ta-implanted samples. This is consistent with density-functional theory calculations which predict that only Nb will form deep-energy levels in the bandgap of HfO₂. Photocurrent spectroscopy exhibits characteristics consistent with one of the trap levels predicted in these calculations. Nb-implanted samples showing memory windows in capacitance–voltage (V) curves always exhibit current (I) peaks in I–V curves, indicating that NVM effects result from deep traps in HfO₂. In contrast, Ta-implanted samples show dielectric breakdowns during the I–V sweeps between 5 and 11 V, consistent with the fact that no trap levels are present. For a sample implanted with a fluence of 10¹³Nb cm⁻², the charge losses after 10⁴ s are ∼9.8 and ∼25.5% at room temperature (RT) and 85°C, respectively, and the expected charge loss after 10 years is ∼34% at RT, very promising for commercial NVMs

Role of G{alpha}12 and G{alpha}13 as Novel Switches for the Activity of Nrf2, a Key Antioxidative Transcription Factor

G{alpha}12 and G{alpha}13 function as molecular regulators responding to extracellular stimuli. NF-E2-related factor 2 (Nrf2) is involved in a protective adaptive response to oxidative stress. This study investigated the regulation of Nrf2 by G{alpha}12 and G{alpha}13. A deficiency of G{alpha}12, but not of G{alpha}13, enhanced Nrf2 activity and target gene transactivation in embryo fibroblasts. In mice, G{alpha}12 knockout activated Nrf2 and thereby facilitated heme catabolism to bilirubin and its glucuronosyl conjugations. An oligonucleotide microarray demonstrated the transactivation of Nrf2 target genes by G{alpha}12 gene knockout. G{alpha}12 deficiency reduced Jun N-terminal protein kinase (JNK)-dependent Nrf2 ubiquitination required for proteasomal degradation, and so did G{alpha}13 deficiency. The absence of G{alpha}12, but not of G{alpha}13, increased protein kinase C {delta} (PKC {delta}) activation and the PKC {delta}-mediated serine phosphorylation of Nrf2. G{alpha}13 gene knockout or knockdown abrogated the Nrf2 phosphorylation induced by G{alpha}12 deficiency, suggesting that relief from G{alpha}12 repression leads to the G{alpha}13-mediated activation of Nrf2. Constitutive activation of G{alpha}13 promoted Nrf2 activity and target gene induction via Rho-mediated PKC {delta} activation, corroborating positive regulation by G{alpha}13. In summary, G{alpha}12 and G{alpha}13 transmit a JNK-dependent signal for Nrf2 ubiquitination, whereas G{alpha}13 regulates Rho-PKC {delta}-mediated Nrf2 phosphorylation, which is negatively balanced by G{alpha}12

Impact time control based on time-to-go prediction for sea-skimming antiship missiles

This paper proposes a novel approach for guidance law design to satisfy the impact-time constraints for a certain class of homing missiles. The proposed guidance law provides proper lateral acceleration commands that make the impact time error converge to zero by the time of impact. This scheme can be applied to any existing guidance law for which a formula of predicted time to go is available. Convergence of time-to-go errors is supported by Lyapunov stability. The optimal guidance law and the impact angle control guidance law are extended by the proposed method for impact-time-control guidance and impact-time-and-angle-control guidance, respectively. The performance of the extended guidance laws is demonstrated by numerical simulation